Sex Differences in the Socioeconomic Gradient of Latent Cardiometabolic Phenotypes in a Working-Age Population from the Balearic Islands (Spain): A Population-Based Analysis

Background: Cardiometabolic diseases are shaped by complex interactions between biological and social determinants. While socioeconomic inequalities in cardiometabolic risk are well established, less is known about how these inequalities are distributed across multidimensional cardiometabolic phenotypes and whether they differ by sex. Objective: We aimed to examine sex differences in the socioeconomic gradient of cardiometabolic phenotypes using latent class analysis in a working-age population. Methods: A cross-sectional study was conducted in 3108 adults aged 18–65 years undergoing occupational health assessments in the Balearic Islands (Spain). Educational level was used as an indicator of socioeconomic position. Cardiometabolic risk was assessed using obesity, insulin resistance (METS-IR), metabolic dysfunction-associated steatotic liver disease (FLI), atherogenic index of plasma, and metabolic syndrome. Latent class analysis was applied to identify cardiometabolic phenotypes. Multinomial logistic regression models stratified by sex and interaction analyses were used to assess associations between educational level and class membership. Tests for linear trend and predicted probabilities were also estimated. Results: Four cardiometabolic phenotypes were identified: low-risk (40.8%), obesity-dominant (24.1%), dysmetabolic (19.3%), and high-risk multimorbid (15.8%). A clear socioeconomic gradient was observed, with lower educational attainment associated with a higher likelihood of belonging to adverse cardiometabolic profiles. This gradient was stronger among women. For the high-risk multimorbid class, the relative risk ratio comparing low vs. high educational level was 1.82 (95% CI 1.34–2.46) in men and 2.47 (95% CI 1.68–3.64) in women (p for interaction = 0.012). A significant linear trend across educational levels was observed in both sexes (p for trend < 0.001). Predicted probabilities further confirmed a steeper increase in high-risk profiles among women with lower educational attainment. Conclusions: Cardiometabolic risk is structured into distinct phenotypic profiles that are socially patterned. Socioeconomic inequalities are strongly associated with adverse cardiometabolic phenotypes, with a more pronounced gradient among women. These findings highlight the need for gender-sensitive strategies addressing social determinants to reduce cardiometabolic health inequalities.